Pepperdine University
Cal Poly - San Luis Obispo
University of Nebraska - Lincoln
University of Nebraska - Lincoln
Motivation and Background
Comprehensive Graphical Testing
Research Goals
Perception through Lineups
Prediction through ‘You Draw It’
Numerical Translation and Estimation
Overall Conclusions and Discussion
Data visualization is defined as the art of drawing graphical charts in order to display data Unwin (2020).
What are graphics useful for? Lewandowsky and Spence (1989)
📉 Data cleaning.
🔍 Exploring data structure.
💬 Communicating information.
Who uses graphics?
Governments (Harms 1991; Playfair 1801; Walker 2013).
Companies (Chandar, Collier, and Miranti 2012; Yates 1985).
News sources and mass media (Aisch et al. 2016).
Scientific publications (Gouretski and Koltermann 2007).
Visit the Timeline of Infographics by RJ Andrews (Info We Trust Data Storyteller).
Graphics are viewed as a mapping from variables in the data set to visual attributes on the chart.
Evaluate design choices and understand cognitive biases through the use of visual tests.
Could ask participants to:
📉 identify differences in graphs.
📖 read information off of a chart accurately.
🌍 use data to make correct real-world decisions.
✏️ predict the next few observations.
Carpenter and Shah (1988) identifies pattern recognition, interpretative processes, and integrative processes as strategies and processes required to complete tasks of varying degrees of complexity.
Pattern recognition requires the viewer to encode graphic patterns.
Interpretive processes operate on those patterns to construct meaning.
Integrative processes then relate the meanings to the contextual scenario as inferred from labels and titles.
When doing exploratory data analysis, how do we know if what we see is actually there?
Embed a target plot (actual data) in a lineup of null plots (randomly permuted data sets).
New York Times (Aisch, Cox, and Quealy 2015)
Eye Fitting Straight Lines in the Modern Era (Robinson, Howard, and VanderPlas 2022)
youdrawitR Package
Von Bergmann (2021)
Burn-Murdoch et al. (2020)
Big Idea: Are there benefits to displaying exponentially increasing data on a log scale rather than a linear scale?
Perception through Lineups – tests an individual’s ability to perceptually differentiate exponentially increasing data with differing rates of change on both the linear and log scale.
Prediction with ‘You Draw It’ – tests an individual’s ability to make predictions for exponentially increasing data.
Estimation by Numerical Translation – tests an individual’s ability to translate a graph of exponentially increasing data into real value quantities.
The series of graphical tests were conducted through an RShiny application found at https://emily-robinson.shinyapps.io/perception-of-statistical-graphics-log/.
Study Participant Prompt: Which plot is most different?
Study Participant Prompt: Use your mouse to fill in the trend in the yellow box region.
Study Participant Prompt: From 4520 to 4540, the population increases by ____ Tribbles [Ewoks].
1. Perception through Lineups
2. Prediction through ‘You Draw It’
3. Numerical Translation and Estimation
Perceptual advantages of the use of log scales due to the change in contextual appearance.
Our understanding of log logic is flawed when translating the information into context.
We recommend consideration of both user needs and graph specific tasks when presenting data on the log scale.
Caution should be taken when interpretation of large magnitudes is required, but advantages may appear when it is necessary to visually identify and interpret small magnitudes on the chart.